Dealing with large geospatial data sets presents many challenges. For this reason, many organizations struggle to take full advantage of geospatial data.

First, there is the sheer volume of geospatial data. For example, it is estimated that 100 TB of weather-related data are generated daily. This alone presents considerable storage and access problems for most organizations. Geospatial data is also stored across many different files, which makes it difficult to find the files that contain the data needed to solve your specific problem.

In addition, geospatial data is stored in many different formats and calibrated by different standards. Any effort to compare, combine or map data first requires a significant amount of data scrubbing and reformatting.

Finally, working with raw geospatial data requires specialized knowledge and the application of advanced mathematics to conduct necessary tasks, such as geospatial alignment of data layers. Unless analysts are proficient and experienced at this work, they will not get value from the data or make progress toward their organization’s business goals.

Geospatial data collection
Because the sheer volume of geospatial data routinely required by enterprises is prohibitively large, many organizations look to using a service to obtain curated geospatial data.

Regardless of where you source your geospatial data, data quality must always be maintained. Poor data results in models of little or limited use. (The cautionary phrase “Bad data in—bad insights out” proves brutally true.) It seems self-evident that organizations can benefit significantly from having a solution in place that curates and checks data, so any “garbage” data gets properly accounted for.

Geospatial data management
With so much data now in abundance, managing it takes on considerable importance. Many organizations are finding themselves overrun with data and are turning to their in-house data scientists to help them manage it.

It has been estimated that as much as 90% of data scientists’ time is spent on data-curation activities, including organizing, “cleaning” and reformatting data. That leaves those data scientists with only 10% of their workday to devote to analyzing data trends and using those insights to help shape business policy.

When a company turns over data collection and management to a solution such as IBM Environmental Intelligence Suite, both data collection and data management activities can be executed more efficiently. The solution is scalable, cloud-based and able to accommodate different file formats.

By using a curated database of optimized information, data scientists can have more time to concentrate on how to use analytic insights and convert them into organizational progress and business impact. 

Although geospatial analysis, as empowered by GIS, was originally used in connection with life sciences such as geology, ecology and epidemiology, its use has since become manifest throughout most industries. Its applications now touch industries as diverse as defense and social sciences. And the insights that geospatial analysis generates affect matters as critically important as natural resource management and national intelligence.

Geospatial analysis lends itself to the study of many things at once, monitoring hundreds or even thousands of events and collecting pertinent data from them. This provides enterprises of all sizes the chance to use data to make more informed business decisions:

• Utilities providers can analyze the performance of hundreds of thousands of miles of power lines.
• Restaurant chains can use geospatial analysis techniques to track incoming shipments from food providers.
• Wind and solar installations can analyze environmental conditions by pinpointing local assets.

Efforts to analyze massive amounts of data have become more challenging in recent years due to a relative explosion within the Internet of Things (IoT). Objects and devices of all types and purposes are now being engineered to be able to transmit data relevant to that device’s performance or protocols. That’s good news for geospatial analysis, which involves a profusion of data in order to glean valuable insights.

Geospatial analytics
Geospatial analytics is when the collection of data achieved through geospatial analysis is combined with a heightened visual approach that maximizes the data’s impact by organizing it according to time and space.

This type of visual data makes it easier for those studying it to derive indications about trends that might be at work. Geospatial analytics can effectively convey the shape and energy of a changing situation. As increasing amounts of data are gathered about that scenario, it becomes easier to spot even more subtle nuances within that situation.

Most experts expect geospatial technology to become increasingly sophisticated, especially as that technology comes into closer contact with machine learning and AI. 

In fact, it is expected that geospatial AI will also come into its own, bringing a geographic element to machine learning. Experts also forecast the arrival of mapping as a service, in which custom maps of remarkably high resolution can be produced for hire, based on consumer or industrial need.

There are aslo new types of vehicles in development that rely expressly on geospatial technology. They will be used in greater frequency—whether they traverse the sky carrying packages (drones) or drive themselves down streets (autonomous vehicles). New applications for these technologies will also be found, such as using drones for aerial-mapping purposes.